2015: A scanning Odyssey - Nikon Super Coolscan LS-9000 ED

Discussion in 'Classic Manual Cameras' started by jdm_von_weinberg, Feb 2, 2015.

  1. (2004) Super Coolscan LS-9000 ED (4000 dpi, 16bit, 4.8D) Firewire

    Abstract:
    I replace my defunct Canonscan FS4000US with a Super Coolscan LS-9000 ED and make comparisons.


    I put this here in Classic Manual Cameras forum since
    1. this may be of particular interest to those still shooting, whether as re-enactment or not, old film cameras
    2. Classic Manual Cameras forum is my favorite of all the forums here, and I didn’t think it would ’scour’ on No Words, my second favorite.

    THE BEGINNING OF THE HUNT:
    (traditional camera-hunter-and-gatherer behavior)


    I shot film from my cub scout days to 2004, but by 2002, my workflow was digital - I got a scanner and switched from Kodachrome slides to color-negative films that I scanned in. I was still using film cameras, however.

    I had got a Canoscan FS4000 US scanner which was slow but seemed to do an decent job. After some false starts, I finally did it right and scanned in my old slides and negatives (long story and links to earlier at http://www.photo.net/casual-conversations-forum/00arR1 ). With duplication (lo-res and hi-res, rescans) and a growing number of digital-camera images this amounts to 570.68 GB on disk for 81,196 images as of now.

    For historical context, here is a full page of scanners available at B&H in November, 2002.
    Lots of people were doing what I was doing and going to scanning of film.

    00d6UB-554585884.jpg
     
  2. In a story told before, an overseas trip in 2004 revealed slight, but definite fogging of my negative film when I returned and scanned in the images. Fixable, but troubling. I decided this was nature’s way of telling me to go digital, which I (and most everybody else, see my Jeremiad at http://www.photo.net/casual-conversations-forum/00ctG6 ) did in that anno mirable, 2004.

    Meanwhile on the scanning front, my old Mac, which was preserved for its Fast SCSI connection for the Canoscan FS4000 US, is a rare Macintosh G4 400Mhz (Yikes! *). The Yikes! model was only sold for a short time (another story best told elsewhere), and the 400MHz version is even rarer.

    Despite its age, this machine runs not only System 9 (although haven’t booted it up for a long time), but it also runs Mac OS X 10.4.11 (Tiger). Because of its processor and the older version of the OS X, Nikon Scan, the software that came with the Nikon Super Coolscan 9000 ED runs just fine.

    However I think there is a possibility I destroyed a developing arachnid civilization when I opened up my old tower Mac for the first time in some years...
    But, eventually I got it all plugged in, disconnecting old SCSI cables on the way (do you remember SCSI termination? I'm happy to say that I don't have to anymore). The Nikon scanner uses Firewire (IEEE 1394), a lot faster in theory.


    Here are pictures of my old and new scanning setup with the former Canon FS4000 US (bottom) and the Super Coolscan LS-9000 ED, respectively.

    00d6UD-554585984.jpg
     
  3. THE RESULTS

    First, I started up VueScan on the machine, and it immediately (after I turned the scanner on, that is) it recognized the Nikon scanner.

    Then, I was able to install the Nikon software. When I tried to scan with the Nikon Scan software, it ran but came up with a "minor error" message in scanning, but that was because I had no film in the 1st cell to be scanned. Details, details. I might add that the Nikon documentation is no less obscure than is VueScan’s, and I am still fumbling my way toward more accurate use of the machine. Hope and Faith are important virtues, aren’t they?

    Anyhow, I went back to VueScan (used it for years anyway), scanned in image number 7 from a strip of film that I had originally scanned with the Canoscan 4000, and voila.

    Here is that first scan from a Kodak C/N ISO 800 negative, scanned on the Canoscan 4000 and on the Nikon 9000.
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
    00d6UF-554586084.jpg
     
  4. This scan, and the others below, are all with all sharpening, dust reduction, and other impedimenta turned off on both scanners. I will try the ICE dust reduction later on, but now I am just comparing the scans - saved in both cases at 8-bit and highest quality jpegs (this makes for ca. 20-25MB files. RAW/tiff at 16-bit files are so large that even my generous HD capacity would be strained. In any case, film grain is resolved at 4000ppi in this format, especially in this ISO 800 example. All following scans were done with the Nikon Scan software.


    So I then dug out some slides, and scanned in some Kodachrome 25 slides, also at 4000ppi. The Coolscan is faster than the Fast SCSI Canoscan, although not blindingly fast.

    On the Coolscan, to batch scan and save 5 Kodachrome slides at 4000ppi from start (thumbnails, set up save parameters, scan and save to disk) took 9 minutes - about 1.8 minutes per slide at 8-bits. This is faster than the Canoscan 4000FS, and about the same as the Canoscan 9000F at a claimed 4000ppi for the latter.

    Here are the side-by-side comparisons. First, the entire slide as scanned on the two scanners:

    00d6UH-554586184.jpg
     
  5. Then, side by side comparisons of 100% crops from each scanned image:

    00d6UK-554586284.jpg
     
  6. Here is an example of a slide of closer object, K25 again:

    00d6UL-554586384.jpg
     
  7. and here are the side by side 100% of the above images:
    00d6UM-554586484.jpg
     
  8. In these cases, the scans as they were saved to disk from the scanners were somewhat different in tone and color, but they become nearly identical when an “Image>Auto Color” is done on each. The pre-processed Nikon scan was fairly blue when scanned with adjustments off. By the way, of course the Canoscan images were scanned some time ago when the machine was still working.

    There is little on which to choose between the two scanner results, I think. This was surprising to me, given that the initial price of the Canoscan FS4000US was about one fourth of the original price of the Super Coolscan LS-9000 ED.

    After a full page of scanner ads in 2002, by 2005 the offerings in Popular Photography by Adorama were down to the following. Like the replacement of LPs by CDs, the transition was quick and steep.

    00d6UN-554586584.jpg
     
  9. The good news is that the LS-9000 scanned the slides flawlessly, once I began to learn the ropes a little. Neither VueScan nor the Nikon Scan software make anything easy and their documentation is equally opaque (Hamrick is a small outfit, but surprising to me for Nikon).



    As noted earlier, the Nikon scan of the ISO 800 C/N film has a little less chromatic noise, but the originally $400 Canon scanner is pretty close to being sharper. Both scans are more than acceptable. As for the Kodachome 25 examples? No differences that cannot be made equal in post-processing.
    In both cases, the scanners at 4000 ppi are resolving film grain and texture (look at the blue sky in the 100%). An increase to scanning above 4000 ppi will yield no additional improvement in the image quality that I can see.


    The good and bad news is also that I won't have to re-scan everything.

    The even better news is that I now have a hi-res scanner for my medium format film.

    Recommendations? Well, if you have lots of time, can tackle the interface problems (it has USB1 and SCSI), and are doing only small batches of slides or film at a time, the Canoscan FS4000US is a bargain with high-quality results.

    Otherwise, while the scans are not noticeably better, the speed and ruggedness of the Super Coolscan LS-9000 ED make it a clear choice for those with thousands of images on slides and film, most particularly since it handles 120 film as well as 135.
    It is finished.
     
  10. Sooo, essentially you are saying that the Canon scanner is better ? Or is it the operator ?
    Les
     
  11. Given that the scans were done years apart and that there are other minor differences in the unprocessed images, I don't think that you can pick out one as better in IQ than the other.
    The Nikon has greater speed, will work well on its own software if you have or buy an older computer that will allow the software to run -- it's no longer supported by Nikon for any platform, so far as I know. You will have to use VueScan or some other program if you want to run the scanner on new PCs or Macs.
    Up til now, when I was using the Canoscan 4000, I have made the scans without processing, and done all repairs, spotting, etc, in Photoshop. I will be trying out the ICE and other features of the Nikon scanner as I go, but my experience so far with 'dust removal' and color adjustments has been that you get better results in PS than in the scanner itself.
    The operator is the same in all cases, and has LOTS of experience in scanning with all kinds of devices having a Repronar, copystands, Spiratone gadgets and all....
     
  12. Additional detail:
    The Kodak ISO 800 C/N image was shot on a Praktiflex (gen. 2) model with a M40 Tessar 5cm lens; the circus shots on a Nikkormat EL with a Nikkor-S 55mm f/1.2 lens.
    And, thanks Gup, I'm having fun here.
     
  13. That B&H catalog page brings back memories ... recent memories ... as two of the scanners listed (Nikon LS-4000 and Epson 2450) are still part of my regular workflow. Amazing workhorse machines.
     
  14. Thanks Les. I had a little trouble loading from the link you gave, but this worked for me
    http://www.fototime.com/ftweb/bin/ft.dll/pictures?userid={DB62658B-ABF8-465C-A4D2-DADCCE1C253C}&inv=B48262629CF3ECB&userid={DB62658B-ABF8-465C-A4D2-DADCCE1C253C}&inv=B48262629CF3ECB
    From my background in VueScan, and the manual, I had got part of the way there, but it's a useful and helpful slide show.
    Later, Les's original link works fine, must have been a hitch on my end...
     
  15. I only used FARE for Kodachrome a few times, with terrible artifacts.You can see why I didn't persist (see below, or "one strike and you're out")
    The infrared procedure in VueScan worked better, but I still found cleaning the slide really carefully before scanning and a few 'tricks' I've worked out with the "context awareness" feature in Photoshop give me really great results. Spotting, as I've said before, can be calming, like knitting, if one doesn't have to do too much of it.
    As I said, I will try out the features before dis-regarding them, but not today.
    00d6Vx-554589084.jpg
     
  16. It has been so long since I tried it, and gave up on it, that I don't remember how much longer it took. I did try it on some other films, but while nothing else was so bad, I felt there was an overall loss of resolution, rather than artifacts of the example kind.
    One of these days, I may get around to posting a discussion of the method I have developed for making manual spotting quicker and easier for uniform areas like sky. On the other hand, I may be the only person in the world so picky that I am still manually spotting images.... :(
    By the way for Latin scholars- it's anno mirabilis, if you don't have spell correction turned on in TextEdit.
     
  17. Nikon Scan with a Coolscan V is "hit or miss" for ICE IR dust removal on Kodachrome. Sometimes it's a lifesaver, and sometimes it causes crazy edge effects.
    I have a different approach to keeping Nikon Scan working in a Mac environment. I have an x86 Mac Mini, so I can't run it native. So I have Parallels, a Windows XP virtual machine, and Nikon Scan in there. I don't think I have color-managed screen display, but it works well. It's my favorite for no-nonsense C-41 scanning.
    I have have spent the big bucks for native SilverFast Ai, to use Kodachrome Q-60 target, really makes accurate color much easier. Even for Kodachrome II, even though the Q-60 target is Kodachrome 64. The Kodachrome dye set must not have changed much.
    SilverFast's NegaFix is sometimes nice, and sometimes annoying. Nice because it makes Ektar 100 easy to scan well. Annoying because there's no support for any film newer than Ektar 100, like the new Portra 160 and Portra 400. (They say the vendor of the test shots went away.)
     
  18. Some of the Nikon scans look a little soft -- you may be hitting the down "depth of focus" challenges with the Nikon scanners. If your film isn't flat, you may need to play with where to select the focus spot on the frame, so that you get it all in focus. I know you can do this with Nikon Scan, not sure if VueScan supports it.
     
  19. The slight bit of softness was my impression too, but this was my first effort with the Naked Coolscan up against the Naked Canoscan. There really isn't any significant distance at less than 100% views with the nose up against the monitor screen. I'll also say that it is difficult in this sort of test to see whether the difference is acutance or contrast. I suspect the latter since the 'information' in both scans seems to be equivalent.
    I have the feeling that in the end, the Coolscan will prove more versatile as I learn more about its tricks and foibles. I know it took me some time to get VueScan all fine-tuned for the Canoscan scans - and all of the Canoscan scans above were done with VueScan so set up.
     
  20. I scanned some Ilford XP2 (chromogenic) film that I had done on my Canoscan 9000F flatbed scanner after my older Canon film scanner had died. I redid did the scans on the Nikon Coolscan 9000 ED. The camera was a Canon VL2 with a Canon 50mm f/1.8 lens.
    As indicated, the Nikon scanner was set at 4000ppi, while I had done the same image at a claimed 4800ppi on the Canoscan 9000F flatbed.
    Here is a 100% crop from each scanner. Both have been processed identically in Nik HDR Efex Pro2 to bring up highlight and shadow detail.
    Pretty clear evidence that the flatbed scanner is not living up to the claims made for it in terms of resolution.
    00d6yu-554687384.jpg
     
  21. Addendum
    I rescanned the Canoscan 9000F scans of Fuji 200 in a Contax IIa with a Helios 50mm lens.
    I found the differences to justify every bit of the trouble and expense to get the Nikon Coolscan 9000ED. (comparisons of those at the end of http://www.photo.net/classic-cameras-forum/00cu64 )
     
  22. JDM, this is a great topic, very
    interesting to see your process and
    results.

    Have you had any good success
    scanning traditional B&W negatives?

    I still occasionally use my Coolscan V.
    I gave up NikonScan for VueScan
    when I switched to a Mac years ago. I
    find it is easy to get satisfactory results
    scanning color E6 or C41, but it's very
    difficult to scan traditional Black &
    White. I've stopped trying, but the best
    results I got were scanning at a lower
    resolution and if course with ICE or
    infrared turned off.
     
  23. What is it you find makes it very difficult to scan B&W, Ben? What would lowering resolution help with?<br>I never found it anything but very easy myself using the 8000/9000s, but i also never used the older Coolscan V.
     
  24. As I said, the B&W above was Ilford XP2, and the Nikon Coolscan 9000ED kept running home to RGB instead of grayscale. Half of a batch would be B&W, the rest RGB. Probably at least partly my still-climbing-the-learning-curve on it.
    Since the Nikon Scan software runs on the old machine I have serving the scanner, I used that for most of the scans so far. I am a big fan of VueScan and I have a feeling that before I'm done I'll be back with that to power the scans. Batch scans seem easier with VueScan, I think, but I'll have to see.
     
  25. More on this scanner and others in terms of dust and noise reduction:
    http://www.photo.net/classic-cameras-forum/00d7jr
     

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